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DialpadDialpadSan Francisco, CA

Sr. Software Engineer

Lead the architecture and development of Dialpad's autonomous Agentic AI platform, building multi-agent orchestration, memory systems, real-time reasoning, and tool execution for enterprise workflows. Requires 10+ years experience, prior technical leadership at Staff/Principal level, and deep expertise in LLM platforms, agent frameworks, and production AI infrastructure.

225k – 252k/yr
On-site10+ YOEML Engineering

About the role

What you’ll do

  • Drive Technical Strategy: Own the architectural roadmap and delivery of Dialpad’s Agentic infrastructure, core orchestration layers, memory architectures, and evaluation/observability systems.
  • Build & Scale: Design and deploy scalable, multi-modal AI agents capable of autonomous support, real-time voice reasoning, and secure API tool execution across complex enterprise workflows.
  • Mentor & Influence: Act as a technical anchor for the organization, raising the engineering bar, mentoring senior peers, and defining technical standards for an AI-native SDLC.
  • Partner Cross-Functionally: Collaborate with leadership across Product, Engineering, and Applied Research to align technical execution with Dialpad’s long-term business strategy.
  • Push the Frontier: Research and implement emerging agent frameworks, LLM inference optimization, advanced retrieval systems, and cutting-edge safety/policy guardrails to keep Dialpad at the absolute forefront of the "era of the agent."

Skills you’ll bring

  • 10+ years of relevant software engineering experience, with a proven track record of technical leadership (as a Staff, Senior Staff, or Principal Engineer) shipping complex, large-scale systems.
  • Strong foundations in scaling distributed systems and production-grade infrastructure before evolving into applied AI, LLM platforms, and agentic architectures.
  • Ability to lead and grow a small group of engineers.
  • Inference optimization and fine-tuning strategies.
  • Advanced retrieval systems and memory architectures.
  • Hands-on experience with frameworks like LangChain/LangGraph, CrewAI, or AWS/Google Agent ecosystems.
  • Evaluation, observability, and safety frameworks for production AI systems.
  • Streaming infrastructure and voice/conversational AI.
  • Tool use, API execution frameworks, and human-in-the-loop validation systems.
  • Experience setting clear technical goals, identifying architectural risks, and systematically clearing tech-debt gaps.
  • Ability to thrive in ambiguity, build cutting-edge AI products from the ground up, and scale them into robust, self-sustaining enterprise systems.

Skills

Distributed Systemsllm platformsagentic architecturesLangChainLangGraphcrewaiinference optimizationretrieval systemsmemory architecturesEvaluation FrameworksObservabilitystreaming infrastructurevoice aiapi tool integrationhuman-in-the-loop systems

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